Nowadays human organizations engage in different levels of understanding what their customers buy and why, what their real needs are, their influencers, and the way they like to be served. However, do these organizations really know their customers?
With new data sciences and analytics tools, customer attention and marketing practices are going through a new revolution based on deeper and broader information, with more engaging and customized stories, and faster business cycles. The new Holy Grail is “segment of one” strategies where information and interaction is personalized to the individual as opposed to traditional aggregated segments. Sentiment analyses using social media (e.g., Facebook, Twitter) can be material in preventing customer churn, a key worry for many companies. However, these tools typically do not accurately reflect a unified and solid vision that meets the growing expectations of customers.
The Internet-Of-Things provides another opportunity to extend people's and systems' awareness with multitudes of intelligent devices in ever more complex configurations, with ever-larger autonomy and cleverness. Data science and analytics tools are being used in this and other scenarios to construct models of complex systems and phenomena. The new generation of applications will inherit the same requirements and limitations of marketing and customers modeling.
Today existing solutions deliver only partially on their promises and require sophisticated and expensive expertise and resources. For example, personalization and omni-channel solutions construct the models from aggregations down, instead of from the individuals up. That is good for some applications, but for understanding a person, they lack a sound, complete, and flexible conceptual modeling framework of all the individual dimensions. Implementing what the customer believes, knows, is interested in, and their decision patterns is limited in scope, capability, and usability.
Current methods are ad-hoc and sometimes disperse annotations that cannot reasonably claim to represent customers' beliefs, interests, or behaviors. Although the growing availability of data sciences and so=called big data algorithms and platforms can generate tremendous business value on specific tasks, they are narrowly scoped and limited in the handling in the generated models. Such models-by-annotation do not offer the capability to simulate or interact with them, and if they model behaviors it is usually through procedural rules that are hard to learn, stabilize, and maintain, and have no adaptability and any other basic cognitive capabilities.
It is recognized that the biggest trouble with most data science models is that they cannot be presented in a concise and consumable way for business personnel to be able to take relevant action. Data scientists can hardly visualize the resulting data that are output from the models, and comprehending what is coming out of such systems is becoming even more complex as machines and algorithms become ever more capable. Filling the gap between such systems and people's understanding requires sophisticated abstractions that must resemble in some form the modular and introspective images of customers, colleagues, friends, and theories of some systems and phenomena.
The conceptual richness that can be attained by such sophisticated abstractions may permit more intuitive data analysis solutions with which we can interact in natural ways, that can give us insight about their function and response to change, that can respond questions about their beliefs and behaviors, that can be used to analyze what-if situations, that can help us understand how to make them behave in some desired ways, and that can allow more autonomous application because of their judgment level. Through applied effort, ingenuity, and innovation, solutions to improve such methods have been realized and are described in connection with embodiments of the present invention.